Overview

Dataset statistics

Number of variables54
Number of observations642
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory271.0 KiB
Average record size in memory432.2 B

Variable types

Numeric18
Categorical36

Warnings

whovoted has constant value "2.0" Constant
better_health is highly correlated with whovotedHigh correlation
health is highly correlated with whovotedHigh correlation
better_environment is highly correlated with whovotedHigh correlation
spouse_edu is highly correlated with whovotedHigh correlation
gov_responsive is highly correlated with whovotedHigh correlation
covid_gov is highly correlated with whovotedHigh correlation
whovoted is highly correlated with better_health and 34 other fieldsHigh correlation
getcovid is highly correlated with whovotedHigh correlation
gov_climate is highly correlated with whovotedHigh correlation
party_reg is highly correlated with whovotedHigh correlation
gov_trust is highly correlated with whovotedHigh correlation
fav_deathpen is highly correlated with whovotedHigh correlation
party_salience is highly correlated with whovotedHigh correlation
stayhome is highly correlated with whovotedHigh correlation
better_taxes is highly correlated with whovotedHigh correlation
interest_campaign is highly correlated with whovotedHigh correlation
interest_politics is highly correlated with whovotedHigh correlation
gov_waste is highly correlated with whovotedHigh correlation
people_trusted is highly correlated with whovotedHigh correlation
lgbtlaw is highly correlated with whovotedHigh correlation
region is highly correlated with whovotedHigh correlation
better_economy is highly correlated with whovotedHigh correlation
inc_gap is highly correlated with whovotedHigh correlation
gov_interests is highly correlated with whovotedHigh correlation
marital is highly correlated with whovotedHigh correlation
children is highly correlated with whovotedHigh correlation
better_covid is highly correlated with whovotedHigh correlation
covid_reopen is highly correlated with whovotedHigh correlation
education is highly correlated with whovotedHigh correlation
armedforces is highly correlated with whovotedHigh correlation
trust_media is highly correlated with whovotedHigh correlation
union is highly correlated with whovotedHigh correlation
gov_corrup is highly correlated with whovotedHigh correlation
better_immigratino is highly correlated with whovotedHigh correlation
satisfied is highly correlated with whovotedHigh correlation
primary_voter is highly correlated with whovotedHigh correlation
caseid has unique values Unique

Reproduction

Analysis started2021-09-23 23:38:08.216161
Analysis finished2021-09-23 23:39:02.447896
Duration54.23 seconds
Software versionpandas-profiling v2.12.0
Download configurationconfig.yaml

Variables

caseid
Real number (ℝ≥0)

UNIQUE

Distinct642
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325661.6168
Minimum200558
Maximum535360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:02.565503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum200558
5-th percentile204334.85
Q1223217.75
median333125
Q3417846.5
95-th percentile507213.5
Maximum535360
Range334802
Interquartile range (IQR)194628.75

Descriptive statistics

Standard deviation100712.2811
Coefficient of variation (CV)0.3092543792
Kurtosis-1.259339057
Mean325661.6168
Median Absolute Deviation (MAD)103540.5
Skewness0.2563804028
Sum209074758
Variance1.014296357 × 1010
MonotonicityStrictly increasing
2021-09-23T19:39:02.687099image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2017281
 
0.2%
4269471
 
0.2%
5221801
 
0.2%
3181211
 
0.2%
3426981
 
0.2%
4440751
 
0.2%
3582421
 
0.2%
2362051
 
0.2%
4410071
 
0.2%
5270241
 
0.2%
Other values (632)632
98.4%
ValueCountFrequency (%)
2005581
0.2%
2008311
0.2%
2010011
0.2%
2010321
0.2%
2010631
0.2%
ValueCountFrequency (%)
5353601
0.2%
5332721
0.2%
5313681
0.2%
5304331
0.2%
5280031
0.2%

interest_politics
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2.0
283 
1.0
173 
3.0
119 
4.0
66 
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
2.0283
44.1%
1.0173
26.9%
3.0119
18.5%
4.066
 
10.3%
5.01
 
0.2%
2021-09-23T19:39:02.897395image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:02.964170image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0283
44.1%
1.0173
26.9%
3.0119
18.5%
4.066
 
10.3%
5.01
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2283
14.7%
1173
 
9.0%
3119
 
6.2%
466
 
3.4%
51
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
2283
22.0%
1173
 
13.5%
3119
 
9.3%
466
 
5.1%
51
 
0.1%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2283
14.7%
1173
 
9.0%
3119
 
6.2%
466
 
3.4%
51
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2283
14.7%
1173
 
9.0%
3119
 
6.2%
466
 
3.4%
51
 
0.1%

interest_campaign
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
1
355 
2
216 
3
71 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters642
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row1
ValueCountFrequency (%)
1355
55.3%
2216
33.6%
371
 
11.1%
2021-09-23T19:39:03.420643image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:03.484429image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1355
55.3%
2216
33.6%
371
 
11.1%

Most occurring characters

ValueCountFrequency (%)
1355
55.3%
2216
33.6%
371
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number642
100.0%

Most frequent character per category

ValueCountFrequency (%)
1355
55.3%
2216
33.6%
371
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common642
100.0%

Most frequent character per script

ValueCountFrequency (%)
1355
55.3%
2216
33.6%
371
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII642
100.0%

Most frequent character per block

ValueCountFrequency (%)
1355
55.3%
2216
33.6%
371
 
11.1%

state_reg
Real number (ℝ≥0)

Distinct31
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.70404984
Minimum2
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:03.556190image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q110.25
median22
Q337
95-th percentile45.9
Maximum54
Range52
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation14.53693824
Coefficient of variation (CV)0.6132681264
Kurtosis-1.327882768
Mean23.70404984
Median Absolute Deviation (MAD)14
Skewness0.1801995182
Sum15218
Variance211.3225733
MonotonicityNot monotonic
2021-09-23T19:39:03.662833image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1282
 
12.8%
672
 
11.2%
4250
 
7.8%
3749
 
7.6%
3638
 
5.9%
830
 
4.7%
4029
 
4.5%
427
 
4.2%
2124
 
3.7%
3424
 
3.7%
Other values (21)217
33.8%
ValueCountFrequency (%)
23
 
0.5%
427
 
4.2%
516
 
2.5%
672
11.2%
830
4.7%
ValueCountFrequency (%)
5412
 
1.9%
4916
 
2.5%
465
 
0.8%
443
 
0.5%
4250
7.8%

party_reg
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2.0
440 
4.0
146 
1.0
52 
5.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row4.0
4th row1.0
5th row2.0
ValueCountFrequency (%)
2.0440
68.5%
4.0146
 
22.7%
1.052
 
8.1%
5.04
 
0.6%
2021-09-23T19:39:03.862165image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:03.926949image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0440
68.5%
4.0146
 
22.7%
1.052
 
8.1%
5.04
 
0.6%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2440
22.8%
4146
 
7.6%
152
 
2.7%
54
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
2440
34.3%
4146
 
11.4%
152
 
4.0%
54
 
0.3%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2440
22.8%
4146
 
7.6%
152
 
2.7%
54
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2440
22.8%
4146
 
7.6%
152
 
2.7%
54
 
0.2%

primary_voter
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
1.0
323 
2.0
319 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row1.0
ValueCountFrequency (%)
1.0323
50.3%
2.0319
49.7%
2021-09-23T19:39:04.112329image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:04.175119image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0323
50.3%
2.0319
49.7%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1323
16.8%
2319
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
1323
25.2%
2319
24.8%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1323
16.8%
2319
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1323
16.8%
2319
16.6%

pol_spectrum
Real number (ℝ≥0)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.520249221
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:04.229935image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.07462344
Coefficient of variation (CV)0.1946693704
Kurtosis0.9069879515
Mean5.520249221
Median Absolute Deviation (MAD)1
Skewness-0.877332964
Sum3544
Variance1.154815538
MonotonicityNot monotonic
2021-09-23T19:39:04.309669image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6303
47.2%
5120
 
18.7%
4108
 
16.8%
793
 
14.5%
39
 
1.4%
27
 
1.1%
12
 
0.3%
ValueCountFrequency (%)
12
 
0.3%
27
 
1.1%
39
 
1.4%
4108
16.8%
5120
18.7%
ValueCountFrequency (%)
793
 
14.5%
6303
47.2%
5120
 
18.7%
4108
 
16.8%
39
 
1.4%

party_id
Real number (ℝ≥0)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.014018692
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:04.394385image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median7
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.298549432
Coefficient of variation (CV)0.2159204183
Kurtosis2.562868767
Mean6.014018692
Median Absolute Deviation (MAD)0
Skewness-1.556936756
Sum3861
Variance1.686230627
MonotonicityNot monotonic
2021-09-23T19:39:04.474119image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7328
51.1%
5136
21.2%
6116
 
18.1%
433
 
5.1%
216
 
2.5%
17
 
1.1%
36
 
0.9%
ValueCountFrequency (%)
17
 
1.1%
216
 
2.5%
36
 
0.9%
433
 
5.1%
5136
21.2%
ValueCountFrequency (%)
7328
51.1%
6116
 
18.1%
5136
21.2%
433
 
5.1%
36
 
0.9%

party_salience
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2.0
161 
5.0
159 
3.0
153 
4.0
88 
1.0
81 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row2.0
4th row5.0
5th row1.0
ValueCountFrequency (%)
2.0161
25.1%
5.0159
24.8%
3.0153
23.8%
4.088
13.7%
1.081
12.6%
2021-09-23T19:39:04.680430image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:04.744218image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0161
25.1%
5.0159
24.8%
3.0153
23.8%
4.088
13.7%
1.081
12.6%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2161
 
8.4%
5159
 
8.3%
3153
 
7.9%
488
 
4.6%
181
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
2161
 
12.5%
5159
 
12.4%
3153
 
11.9%
488
 
6.9%
181
 
6.3%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2161
 
8.4%
5159
 
8.3%
3153
 
7.9%
488
 
4.6%
181
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2161
 
8.4%
5159
 
8.3%
3153
 
7.9%
488
 
4.6%
181
 
4.2%

gov_trust
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
4.0
292 
3.0
198 
2.0
96 
5.0
50 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row3.0
4th row4.0
5th row2.0
ValueCountFrequency (%)
4.0292
45.5%
3.0198
30.8%
2.096
 
15.0%
5.050
 
7.8%
1.06
 
0.9%
2021-09-23T19:39:04.925611image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:04.990392image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0292
45.5%
3.0198
30.8%
2.096
 
15.0%
5.050
 
7.8%
1.06
 
0.9%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
4292
15.2%
3198
 
10.3%
296
 
5.0%
550
 
2.6%
16
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
4292
22.7%
3198
 
15.4%
296
 
7.5%
550
 
3.9%
16
 
0.5%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
4292
15.2%
3198
 
10.3%
296
 
5.0%
550
 
2.6%
16
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
4292
15.2%
3198
 
10.3%
296
 
5.0%
550
 
2.6%
16
 
0.3%

gov_interests
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
1.0
526 
2.0
116 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0526
81.9%
2.0116
 
18.1%
2021-09-23T19:39:05.159825image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:05.222615image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0526
81.9%
2.0116
 
18.1%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1526
27.3%
2116
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
1526
41.0%
2116
 
9.0%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1526
27.3%
2116
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1526
27.3%
2116
 
6.0%

gov_waste
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
1.0
499 
2.0
135 
3.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0499
77.7%
2.0135
 
21.0%
3.08
 
1.2%
2021-09-23T19:39:05.388062image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:05.451848image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0499
77.7%
2.0135
 
21.0%
3.08
 
1.2%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1499
25.9%
2135
 
7.0%
38
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
1499
38.9%
2135
 
10.5%
38
 
0.6%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1499
25.9%
2135
 
7.0%
38
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1499
25.9%
2135
 
7.0%
38
 
0.4%

gov_corrup
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
3.0
244 
2.0
205 
4.0
169 
1.0
 
17
5.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row4.0
5th row3.0
ValueCountFrequency (%)
3.0244
38.0%
2.0205
31.9%
4.0169
26.3%
1.017
 
2.6%
5.07
 
1.1%
2021-09-23T19:39:05.622278image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:05.688058image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0244
38.0%
2.0205
31.9%
4.0169
26.3%
1.017
 
2.6%
5.07
 
1.1%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3244
 
12.7%
2205
 
10.6%
4169
 
8.8%
117
 
0.9%
57
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
3244
 
19.0%
2205
 
16.0%
4169
 
13.2%
117
 
1.3%
57
 
0.5%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3244
 
12.7%
2205
 
10.6%
4169
 
8.8%
117
 
0.9%
57
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3244
 
12.7%
2205
 
10.6%
4169
 
8.8%
117
 
0.9%
57
 
0.4%

people_trusted
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2.0
343 
3.0
163 
4.0
123 
5.0
 
8
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0
ValueCountFrequency (%)
2.0343
53.4%
3.0163
25.4%
4.0123
 
19.2%
5.08
 
1.2%
1.05
 
0.8%
2021-09-23T19:39:05.879421image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:05.945198image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0343
53.4%
3.0163
25.4%
4.0123
 
19.2%
5.08
 
1.2%
1.05
 
0.8%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2343
17.8%
3163
 
8.5%
4123
 
6.4%
58
 
0.4%
15
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
2343
26.7%
3163
 
12.7%
4123
 
9.6%
58
 
0.6%
15
 
0.4%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2343
17.8%
3163
 
8.5%
4123
 
6.4%
58
 
0.4%
15
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2343
17.8%
3163
 
8.5%
4123
 
6.4%
58
 
0.4%
15
 
0.3%

gov_responsive
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2.0
350 
1.0
215 
3.0
77 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row2.0
5th row1.0
ValueCountFrequency (%)
2.0350
54.5%
1.0215
33.5%
3.077
 
12.0%
2021-09-23T19:39:06.118618image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:06.181408image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0350
54.5%
1.0215
33.5%
3.077
 
12.0%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2350
18.2%
1215
 
11.2%
377
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
2350
27.3%
1215
 
16.7%
377
 
6.0%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2350
18.2%
1215
 
11.2%
377
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2350
18.2%
1215
 
11.2%
377
 
4.0%

better_economy
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
5.0
440 
4.0
149 
3.0
 
42
2.0
 
6
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row5.0
4th row4.0
5th row5.0
ValueCountFrequency (%)
5.0440
68.5%
4.0149
 
23.2%
3.042
 
6.5%
2.06
 
0.9%
1.05
 
0.8%
2021-09-23T19:39:06.340876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:06.407654image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0440
68.5%
4.0149
 
23.2%
3.042
 
6.5%
2.06
 
0.9%
1.05
 
0.8%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
5440
22.8%
4149
 
7.7%
342
 
2.2%
26
 
0.3%
15
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
5440
34.3%
4149
 
11.6%
342
 
3.3%
26
 
0.5%
15
 
0.4%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
5440
22.8%
4149
 
7.7%
342
 
2.2%
26
 
0.3%
15
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
5440
22.8%
4149
 
7.7%
342
 
2.2%
26
 
0.3%
15
 
0.3%

better_health
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
5.0
273 
4.0
183 
3.0
138 
2.0
37 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row5.0
4th row4.0
5th row5.0
ValueCountFrequency (%)
5.0273
42.5%
4.0183
28.5%
3.0138
21.5%
2.037
 
5.8%
1.011
 
1.7%
2021-09-23T19:39:06.590044image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:06.654827image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0273
42.5%
4.0183
28.5%
3.0138
21.5%
2.037
 
5.8%
1.011
 
1.7%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
5273
14.2%
4183
 
9.5%
3138
 
7.2%
237
 
1.9%
111
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
5273
21.3%
4183
 
14.3%
3138
 
10.7%
237
 
2.9%
111
 
0.9%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
5273
14.2%
4183
 
9.5%
3138
 
7.2%
237
 
1.9%
111
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
5273
14.2%
4183
 
9.5%
3138
 
7.2%
237
 
1.9%
111
 
0.6%

better_immigratino
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
5.0
448 
4.0
125 
3.0
48 
2.0
 
13
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row5.0
4th row5.0
5th row5.0
ValueCountFrequency (%)
5.0448
69.8%
4.0125
 
19.5%
3.048
 
7.5%
2.013
 
2.0%
1.08
 
1.2%
2021-09-23T19:39:06.826251image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:06.892031image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0448
69.8%
4.0125
 
19.5%
3.048
 
7.5%
2.013
 
2.0%
1.08
 
1.2%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
5448
23.3%
4125
 
6.5%
348
 
2.5%
213
 
0.7%
18
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
5448
34.9%
4125
 
9.7%
348
 
3.7%
213
 
1.0%
18
 
0.6%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
5448
23.3%
4125
 
6.5%
348
 
2.5%
213
 
0.7%
18
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
5448
23.3%
4125
 
6.5%
348
 
2.5%
213
 
0.7%
18
 
0.4%

better_taxes
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
5.0
383 
4.0
154 
3.0
90 
2.0
 
12
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row2.0
3rd row5.0
4th row3.0
5th row5.0
ValueCountFrequency (%)
5.0383
59.7%
4.0154
24.0%
3.090
 
14.0%
2.012
 
1.9%
1.03
 
0.5%
2021-09-23T19:39:07.087378image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:07.153157image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0383
59.7%
4.0154
24.0%
3.090
 
14.0%
2.012
 
1.9%
1.03
 
0.5%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
5383
19.9%
4154
 
8.0%
390
 
4.7%
212
 
0.6%
13
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
5383
29.8%
4154
 
12.0%
390
 
7.0%
212
 
0.9%
13
 
0.2%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
5383
19.9%
4154
 
8.0%
390
 
4.7%
212
 
0.6%
13
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
5383
19.9%
4154
 
8.0%
390
 
4.7%
212
 
0.6%
13
 
0.2%

better_environment
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
3.0
190 
5.0
168 
2.0
126 
4.0
126 
1.0
32 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row4.0
4th row2.0
5th row5.0
ValueCountFrequency (%)
3.0190
29.6%
5.0168
26.2%
2.0126
19.6%
4.0126
19.6%
1.032
 
5.0%
2021-09-23T19:39:07.336547image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:07.400331image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0190
29.6%
5.0168
26.2%
2.0126
19.6%
4.0126
19.6%
1.032
 
5.0%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3190
 
9.9%
5168
 
8.7%
4126
 
6.5%
2126
 
6.5%
132
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
3190
 
14.8%
5168
 
13.1%
4126
 
9.8%
2126
 
9.8%
132
 
2.5%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3190
 
9.9%
5168
 
8.7%
4126
 
6.5%
2126
 
6.5%
132
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3190
 
9.9%
5168
 
8.7%
4126
 
6.5%
2126
 
6.5%
132
 
1.7%

better_covid
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
5.0
254 
3.0
219 
4.0
138 
2.0
 
19
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row5.0
4th row3.0
5th row5.0
ValueCountFrequency (%)
5.0254
39.6%
3.0219
34.1%
4.0138
21.5%
2.019
 
3.0%
1.012
 
1.9%
2021-09-23T19:39:07.581727image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:07.647506image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0254
39.6%
3.0219
34.1%
4.0138
21.5%
2.019
 
3.0%
1.012
 
1.9%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
5254
 
13.2%
3219
 
11.4%
4138
 
7.2%
219
 
1.0%
112
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
5254
 
19.8%
3219
 
17.1%
4138
 
10.7%
219
 
1.5%
112
 
0.9%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
5254
 
13.2%
3219
 
11.4%
4138
 
7.2%
219
 
1.0%
112
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
5254
 
13.2%
3219
 
11.4%
4138
 
7.2%
219
 
1.0%
112
 
0.6%

fav_deathpen
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
1.0
398 
2.0
150 
3.0
58 
4.0
 
36

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row1.0
4th row2.0
5th row1.0
ValueCountFrequency (%)
1.0398
62.0%
2.0150
 
23.4%
3.058
 
9.0%
4.036
 
5.6%
2021-09-23T19:39:07.830893image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:07.894680image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0398
62.0%
2.0150
 
23.4%
3.058
 
9.0%
4.036
 
5.6%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1398
20.7%
2150
 
7.8%
358
 
3.0%
436
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
1398
31.0%
2150
 
11.7%
358
 
4.5%
436
 
2.8%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1398
20.7%
2150
 
7.8%
358
 
3.0%
436
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1398
20.7%
2150
 
7.8%
358
 
3.0%
436
 
1.9%

stayhome
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
3.0
255 
4.0
211 
2.0
99 
1.0
77 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row4.0
4th row4.0
5th row4.0
ValueCountFrequency (%)
3.0255
39.7%
4.0211
32.9%
2.099
 
15.4%
1.077
 
12.0%
2021-09-23T19:39:08.081054image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:08.145837image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0255
39.7%
4.0211
32.9%
2.099
 
15.4%
1.077
 
12.0%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3255
 
13.2%
4211
 
11.0%
299
 
5.1%
177
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
3255
 
19.9%
4211
 
16.4%
299
 
7.7%
177
 
6.0%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3255
 
13.2%
4211
 
11.0%
299
 
5.1%
177
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3255
 
13.2%
4211
 
11.0%
299
 
5.1%
177
 
4.0%

trust_media
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
1.0
398 
2.0
170 
3.0
57 
4.0
 
12
5.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row2.0
5th row1.0
ValueCountFrequency (%)
1.0398
62.0%
2.0170
26.5%
3.057
 
8.9%
4.012
 
1.9%
5.05
 
0.8%
2021-09-23T19:39:08.329226image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:08.397996image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0398
62.0%
2.0170
26.5%
3.057
 
8.9%
4.012
 
1.9%
5.05
 
0.8%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1398
20.7%
2170
 
8.8%
357
 
3.0%
412
 
0.6%
55
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
1398
31.0%
2170
 
13.2%
357
 
4.4%
412
 
0.9%
55
 
0.4%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1398
20.7%
2170
 
8.8%
357
 
3.0%
412
 
0.6%
55
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1398
20.7%
2170
 
8.8%
357
 
3.0%
412
 
0.6%
55
 
0.3%

corr_trump
Real number (ℝ≥0)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.179127726
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:08.467830image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.597775517
Coefficient of variation (CV)0.3823227289
Kurtosis-0.1905879919
Mean4.179127726
Median Absolute Deviation (MAD)1
Skewness-0.308489469
Sum2683
Variance2.552886601
MonotonicityNot monotonic
2021-09-23T19:39:08.545501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4317
49.4%
6105
 
16.4%
164
 
10.0%
554
 
8.4%
747
 
7.3%
243
 
6.7%
312
 
1.9%
ValueCountFrequency (%)
164
 
10.0%
243
 
6.7%
312
 
1.9%
4317
49.4%
554
 
8.4%
ValueCountFrequency (%)
747
 
7.3%
6105
 
16.4%
554
 
8.4%
4317
49.4%
312
 
1.9%

fav_impeach
Real number (ℝ≥0)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.465732087
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:08.630217image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median7
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.20823107
Coefficient of variation (CV)0.1868668626
Kurtosis6.295144235
Mean6.465732087
Median Absolute Deviation (MAD)0
Skewness-2.533349541
Sum4151
Variance1.459822318
MonotonicityNot monotonic
2021-09-23T19:39:08.710947image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7502
78.2%
459
 
9.2%
651
 
7.9%
514
 
2.2%
19
 
1.4%
25
 
0.8%
32
 
0.3%
ValueCountFrequency (%)
19
 
1.4%
25
 
0.8%
32
 
0.3%
459
9.2%
514
 
2.2%
ValueCountFrequency (%)
7502
78.2%
651
 
7.9%
514
 
2.2%
459
 
9.2%
32
 
0.3%

fav_senacquittal
Real number (ℝ≥0)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.580996885
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:08.797657image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.246704337
Coefficient of variation (CV)0.7885558466
Kurtosis5.156653211
Mean1.580996885
Median Absolute Deviation (MAD)0
Skewness2.349016976
Sum1015
Variance1.554271704
MonotonicityNot monotonic
2021-09-23T19:39:08.876394image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1489
76.2%
469
 
10.7%
260
 
9.3%
78
 
1.2%
68
 
1.2%
37
 
1.1%
51
 
0.2%
ValueCountFrequency (%)
1489
76.2%
260
 
9.3%
37
 
1.1%
469
 
10.7%
51
 
0.2%
ValueCountFrequency (%)
78
 
1.2%
68
 
1.2%
51
 
0.2%
469
10.7%
37
 
1.1%

covid_gov
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
3.0
475 
4.0
71 
5.0
71 
1.0
 
14
2.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row5.0
3rd row3.0
4th row3.0
5th row3.0
ValueCountFrequency (%)
3.0475
74.0%
4.071
 
11.1%
5.071
 
11.1%
1.014
 
2.2%
2.011
 
1.7%
2021-09-23T19:39:09.067754image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:09.135530image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0475
74.0%
4.071
 
11.1%
5.071
 
11.1%
1.014
 
2.2%
2.011
 
1.7%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3475
24.7%
571
 
3.7%
471
 
3.7%
114
 
0.7%
211
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
3475
37.0%
571
 
5.5%
471
 
5.5%
114
 
1.1%
211
 
0.9%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3475
24.7%
571
 
3.7%
471
 
3.7%
114
 
0.7%
211
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3475
24.7%
571
 
3.7%
471
 
3.7%
114
 
0.7%
211
 
0.6%

covid_reopen
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
3.0
297 
5.0
158 
4.0
117 
1.0
37 
2.0
33 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row2.0
3rd row3.0
4th row2.0
5th row3.0
ValueCountFrequency (%)
3.0297
46.3%
5.0158
24.6%
4.0117
 
18.2%
1.037
 
5.8%
2.033
 
5.1%
2021-09-23T19:39:09.316921image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:09.382702image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0297
46.3%
5.0158
24.6%
4.0117
 
18.2%
1.037
 
5.8%
2.033
 
5.1%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3297
15.4%
5158
 
8.2%
4117
 
6.1%
137
 
1.9%
233
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
3297
23.1%
5158
 
12.3%
4117
 
9.1%
137
 
2.9%
233
 
2.6%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3297
15.4%
5158
 
8.2%
4117
 
6.1%
137
 
1.9%
233
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3297
15.4%
5158
 
8.2%
4117
 
6.1%
137
 
1.9%
233
 
1.7%

inc_gap
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
3.0
248 
1.0
230 
2.0
125 
4.0
31 
5.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row3.0
4th row3.0
5th row3.0
ValueCountFrequency (%)
3.0248
38.6%
1.0230
35.8%
2.0125
19.5%
4.031
 
4.8%
5.08
 
1.2%
2021-09-23T19:39:09.563097image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:09.628876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0248
38.6%
1.0230
35.8%
2.0125
19.5%
4.031
 
4.8%
5.08
 
1.2%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3248
 
12.9%
1230
 
11.9%
2125
 
6.5%
431
 
1.6%
58
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
3248
 
19.3%
1230
 
17.9%
2125
 
9.7%
431
 
2.4%
58
 
0.6%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3248
 
12.9%
1230
 
11.9%
2125
 
6.5%
431
 
1.6%
58
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3248
 
12.9%
1230
 
11.9%
2125
 
6.5%
431
 
1.6%
58
 
0.4%

gov_climate
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
3
393 
2
128 
1
120 
-9
 
1

Length

Max length2
Median length1
Mean length1.001557632
Min length1

Characters and Unicode

Total characters643
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row3
ValueCountFrequency (%)
3393
61.2%
2128
 
19.9%
1120
 
18.7%
-91
 
0.2%
2021-09-23T19:39:09.826216image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:09.892993image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
3393
61.2%
2128
 
19.9%
1120
 
18.7%
91
 
0.2%

Most occurring characters

ValueCountFrequency (%)
3393
61.1%
2128
 
19.9%
1120
 
18.7%
-1
 
0.2%
91
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number642
99.8%
Dash Punctuation1
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
3393
61.2%
2128
 
19.9%
1120
 
18.7%
91
 
0.2%
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common643
100.0%

Most frequent character per script

ValueCountFrequency (%)
3393
61.1%
2128
 
19.9%
1120
 
18.7%
-1
 
0.2%
91
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII643
100.0%

Most frequent character per block

ValueCountFrequency (%)
3393
61.1%
2128
 
19.9%
1120
 
18.7%
-1
 
0.2%
91
 
0.2%

samesex
Real number (ℝ≥0)

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.238317757
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:09.955783image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.692664445
Coefficient of variation (CV)0.7562216934
Kurtosis0.0007556083611
Mean2.238317757
Median Absolute Deviation (MAD)0
Skewness1.20275033
Sum1437
Variance2.865112922
MonotonicityNot monotonic
2021-09-23T19:39:10.044486image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1329
51.2%
2135
21.0%
658
 
9.0%
553
 
8.3%
343
 
6.7%
424
 
3.7%
ValueCountFrequency (%)
1329
51.2%
2135
21.0%
343
 
6.7%
424
 
3.7%
553
 
8.3%
ValueCountFrequency (%)
658
9.0%
553
 
8.3%
424
 
3.7%
343
 
6.7%
2135
21.0%

transgender
Real number (ℝ≥0)

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.163551402
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:10.131196image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.624609447
Coefficient of variation (CV)0.7508993987
Kurtosis-0.1800780259
Mean2.163551402
Median Absolute Deviation (MAD)0
Skewness1.131421171
Sum1389
Variance2.639355855
MonotonicityNot monotonic
2021-09-23T19:39:10.219899image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1360
56.1%
295
 
14.8%
568
 
10.6%
446
 
7.2%
341
 
6.4%
632
 
5.0%
ValueCountFrequency (%)
1360
56.1%
295
 
14.8%
341
 
6.4%
446
 
7.2%
568
 
10.6%
ValueCountFrequency (%)
632
 
5.0%
568
10.6%
446
7.2%
341
6.4%
295
14.8%

lgbtlaw
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
1.0
364 
2.0
152 
4.0
75 
3.0
51 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row1.0
5th row4.0
ValueCountFrequency (%)
1.0364
56.7%
2.0152
23.7%
4.075
 
11.7%
3.051
 
7.9%
2021-09-23T19:39:10.419234image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:10.483021image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0364
56.7%
2.0152
23.7%
4.075
 
11.7%
3.051
 
7.9%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1364
18.9%
2152
 
7.9%
475
 
3.9%
351
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
1364
28.3%
2152
 
11.8%
475
 
5.8%
351
 
4.0%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1364
18.9%
2152
 
7.9%
475
 
3.9%
351
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1364
18.9%
2152
 
7.9%
475
 
3.9%
351
 
2.6%

birthright
Real number (ℝ≥0)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.255451713
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:10.547803image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q34
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.037138
Coefficient of variation (CV)0.6257620076
Kurtosis-1.031794436
Mean3.255451713
Median Absolute Deviation (MAD)2
Skewness0.422928725
Sum2090
Variance4.149931231
MonotonicityNot monotonic
2021-09-23T19:39:10.628576image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1200
31.2%
4192
29.9%
292
14.3%
766
 
10.3%
654
 
8.4%
319
 
3.0%
519
 
3.0%
ValueCountFrequency (%)
1200
31.2%
292
14.3%
319
 
3.0%
4192
29.9%
519
 
3.0%
ValueCountFrequency (%)
766
 
10.3%
654
 
8.4%
519
 
3.0%
4192
29.9%
319
 
3.0%

deportkids
Real number (ℝ≥0)

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.316199377
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:10.711298image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median5
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.531169409
Coefficient of variation (CV)0.3547494626
Kurtosis-0.1961146962
Mean4.316199377
Median Absolute Deviation (MAD)1
Skewness-0.9420067952
Sum2771
Variance2.34447976
MonotonicityNot monotonic
2021-09-23T19:39:10.792983image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5254
39.6%
6134
20.9%
4106
16.5%
158
 
9.0%
255
 
8.6%
335
 
5.5%
ValueCountFrequency (%)
158
 
9.0%
255
 
8.6%
335
 
5.5%
4106
16.5%
5254
39.6%
ValueCountFrequency (%)
6134
20.9%
5254
39.6%
4106
16.5%
335
 
5.5%
255
 
8.6%

wall
Real number (ℝ≥0)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.947040498
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:10.873713image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.484290791
Coefficient of variation (CV)0.7623317504
Kurtosis1.887231691
Mean1.947040498
Median Absolute Deviation (MAD)0
Skewness1.601497025
Sum1250
Variance2.203119153
MonotonicityNot monotonic
2021-09-23T19:39:10.955441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1394
61.4%
4100
 
15.6%
291
 
14.2%
326
 
4.0%
714
 
2.2%
613
 
2.0%
54
 
0.6%
ValueCountFrequency (%)
1394
61.4%
291
 
14.2%
326
 
4.0%
4100
 
15.6%
54
 
0.6%
ValueCountFrequency (%)
714
 
2.2%
613
 
2.0%
54
 
0.6%
4100
15.6%
326
 
4.0%

russianinterfere
Real number (ℝ)

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.144859813
Minimum-9
Maximum5
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.3%
Memory size5.1 KiB
2021-09-23T19:39:11.042149image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile2
Q13
median5
Q35
95-th percentile5
Maximum5
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3168345
Coefficient of variation (CV)0.3177030248
Kurtosis29.85472397
Mean4.144859813
Median Absolute Deviation (MAD)0
Skewness-3.735079421
Sum2661
Variance1.7340531
MonotonicityNot monotonic
2021-09-23T19:39:11.119889image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5357
55.6%
4122
 
19.0%
3103
 
16.0%
239
 
6.1%
119
 
3.0%
-92
 
0.3%
ValueCountFrequency (%)
-92
 
0.3%
119
 
3.0%
239
 
6.1%
3103
16.0%
4122
19.0%
ValueCountFrequency (%)
5357
55.6%
4122
 
19.0%
3103
 
16.0%
239
 
6.1%
119
 
3.0%

religion
Real number (ℝ≥0)

Distinct8
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.347352025
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:11.206599image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q36
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.397847611
Coefficient of variation (CV)0.55156509
Kurtosis-0.7581970454
Mean4.347352025
Median Absolute Deviation (MAD)1
Skewness0.2557998805
Sum2791
Variance5.749673164
MonotonicityNot monotonic
2021-09-23T19:39:11.293309image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5151
23.5%
2127
19.8%
6106
16.5%
194
14.6%
476
11.8%
959
 
9.2%
715
 
2.3%
814
 
2.2%
ValueCountFrequency (%)
194
14.6%
2127
19.8%
476
11.8%
5151
23.5%
6106
16.5%
ValueCountFrequency (%)
959
 
9.2%
814
 
2.2%
715
 
2.3%
6106
16.5%
5151
23.5%

age
Real number (ℝ≥0)

Distinct57
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.98598131
Minimum23
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:11.403939image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile31
Q141
median56
Q367
95-th percentile80
Maximum80
Range57
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.17525666
Coefficient of variation (CV)0.2759841018
Kurtosis-1.090634982
Mean54.98598131
Median Absolute Deviation (MAD)13
Skewness-0.09296112632
Sum35301
Variance230.2884147
MonotonicityNot monotonic
2021-09-23T19:39:11.525534image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8036
 
5.6%
6622
 
3.4%
3919
 
3.0%
3818
 
2.8%
6918
 
2.8%
5917
 
2.6%
5817
 
2.6%
6717
 
2.6%
5416
 
2.5%
4916
 
2.5%
Other values (47)446
69.5%
ValueCountFrequency (%)
232
 
0.3%
243
0.5%
263
0.5%
277
1.1%
283
0.5%
ValueCountFrequency (%)
8036
5.6%
792
 
0.3%
788
 
1.2%
778
 
1.2%
767
 
1.1%

marital
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
1.0
592 
4.0
 
24
6.0
 
21
3.0
 
4
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0592
92.2%
4.024
 
3.7%
6.021
 
3.3%
3.04
 
0.6%
5.01
 
0.2%
2021-09-23T19:39:11.745796image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:12.208248image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0592
92.2%
4.024
 
3.7%
6.021
 
3.3%
3.04
 
0.6%
5.01
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1592
30.7%
424
 
1.2%
621
 
1.1%
34
 
0.2%
51
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
1592
46.1%
424
 
1.9%
621
 
1.6%
34
 
0.3%
51
 
0.1%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1592
30.7%
424
 
1.2%
621
 
1.1%
34
 
0.2%
51
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
1592
30.7%
424
 
1.2%
621
 
1.1%
34
 
0.2%
51
 
0.1%

education
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
3.0
232 
4.0
177 
5.0
127 
2.0
86 
1.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row5.0
3rd row3.0
4th row4.0
5th row3.0
ValueCountFrequency (%)
3.0232
36.1%
4.0177
27.6%
5.0127
19.8%
2.086
 
13.4%
1.020
 
3.1%
2021-09-23T19:39:12.371702image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:12.436485image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0232
36.1%
4.0177
27.6%
5.0127
19.8%
2.086
 
13.4%
1.020
 
3.1%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3232
 
12.0%
4177
 
9.2%
5127
 
6.6%
286
 
4.5%
120
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
3232
 
18.1%
4177
 
13.8%
5127
 
9.9%
286
 
6.7%
120
 
1.6%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3232
 
12.0%
4177
 
9.2%
5127
 
6.6%
286
 
4.5%
120
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3232
 
12.0%
4177
 
9.2%
5127
 
6.6%
286
 
4.5%
120
 
1.0%

spouse_edu
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
3.0
227 
4.0
173 
2.0
121 
5.0
99 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0
ValueCountFrequency (%)
3.0227
35.4%
4.0173
26.9%
2.0121
18.8%
5.099
15.4%
1.022
 
3.4%
2021-09-23T19:39:12.602929image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:12.665720image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0227
35.4%
4.0173
26.9%
2.0121
18.8%
5.099
15.4%
1.022
 
3.4%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3227
 
11.8%
4173
 
9.0%
2121
 
6.3%
599
 
5.1%
122
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
3227
 
17.7%
4173
 
13.5%
2121
 
9.4%
599
 
7.7%
122
 
1.7%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3227
 
11.8%
4173
 
9.0%
2121
 
6.3%
599
 
5.1%
122
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3227
 
11.8%
4173
 
9.0%
2121
 
6.3%
599
 
5.1%
122
 
1.1%

armedforces
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
3.0
530 
2.0
109 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0
ValueCountFrequency (%)
3.0530
82.6%
2.0109
 
17.0%
1.03
 
0.5%
2021-09-23T19:39:12.843126image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:12.905915image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0530
82.6%
2.0109
 
17.0%
1.03
 
0.5%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3530
27.5%
2109
 
5.7%
13
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
3530
41.3%
2109
 
8.5%
13
 
0.2%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3530
27.5%
2109
 
5.7%
13
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
3530
27.5%
2109
 
5.7%
13
 
0.2%

labor
Real number (ℝ≥0)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.707165109
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:12.966712image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.236747081
Coefficient of variation (CV)0.8262322359
Kurtosis-1.162153572
Mean2.707165109
Median Absolute Deviation (MAD)0
Skewness0.7164864631
Sum1738
Variance5.003037505
MonotonicityNot monotonic
2021-09-23T19:39:13.054420image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1387
60.3%
5166
25.9%
755
 
8.6%
215
 
2.3%
611
 
1.7%
46
 
0.9%
82
 
0.3%
ValueCountFrequency (%)
1387
60.3%
215
 
2.3%
46
 
0.9%
5166
25.9%
611
 
1.7%
ValueCountFrequency (%)
82
 
0.3%
755
 
8.6%
611
 
1.7%
5166
25.9%
46
 
0.9%

union
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2.0
547 
1.0
95 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row2.0
ValueCountFrequency (%)
2.0547
85.2%
1.095
 
14.8%
2021-09-23T19:39:13.244781image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:13.306575image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0547
85.2%
1.095
 
14.8%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2547
28.4%
195
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
2547
42.6%
195
 
7.4%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2547
28.4%
195
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2547
28.4%
195
 
4.9%

ethnicity
Real number (ℝ≥0)

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.426791277
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:13.361392image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.168395028
Coefficient of variation (CV)0.8188969522
Kurtosis6.549710534
Mean1.426791277
Median Absolute Deviation (MAD)0
Skewness2.758639936
Sum916
Variance1.365146942
MonotonicityNot monotonic
2021-09-23T19:39:13.448102image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1554
86.3%
330
 
4.7%
518
 
2.8%
417
 
2.6%
617
 
2.6%
26
 
0.9%
ValueCountFrequency (%)
1554
86.3%
26
 
0.9%
330
 
4.7%
417
 
2.6%
518
 
2.8%
ValueCountFrequency (%)
617
2.6%
518
2.8%
417
2.6%
330
4.7%
26
 
0.9%

children
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
0.0
399 
2.0
99 
1.0
83 
3.0
43 
4.0
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0399
62.1%
2.099
 
15.4%
1.083
 
12.9%
3.043
 
6.7%
4.018
 
2.8%
2021-09-23T19:39:13.647435image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:13.713215image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0399
62.1%
2.099
 
15.4%
1.083
 
12.9%
3.043
 
6.7%
4.018
 
2.8%

Most occurring characters

ValueCountFrequency (%)
01041
54.0%
.642
33.3%
299
 
5.1%
183
 
4.3%
343
 
2.2%
418
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
01041
81.1%
299
 
7.7%
183
 
6.5%
343
 
3.3%
418
 
1.4%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
01041
54.0%
.642
33.3%
299
 
5.1%
183
 
4.3%
343
 
2.2%
418
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
01041
54.0%
.642
33.3%
299
 
5.1%
183
 
4.3%
343
 
2.2%
418
 
0.9%

income
Real number (ℝ≥0)

Distinct22
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.01401869
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2021-09-23T19:39:13.793945image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median16
Q319
95-th percentile22
Maximum22
Range21
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.171606495
Coefficient of variation (CV)0.4403880593
Kurtosis-0.5459119196
Mean14.01401869
Median Absolute Deviation (MAD)4
Skewness-0.6833504855
Sum8997
Variance38.08872673
MonotonicityNot monotonic
2021-09-23T19:39:13.889625image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1764
 
10.0%
1952
 
8.1%
2151
 
7.9%
148
 
7.5%
1046
 
7.2%
2244
 
6.9%
1844
 
6.9%
2037
 
5.8%
1534
 
5.3%
1633
 
5.1%
Other values (12)189
29.4%
ValueCountFrequency (%)
148
7.5%
26
 
0.9%
34
 
0.6%
49
 
1.4%
516
 
2.5%
ValueCountFrequency (%)
2244
6.9%
2151
7.9%
2037
5.8%
1952
8.1%
1844
6.9%

health
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2.0
244 
3.0
199 
1.0
112 
4.0
72 
5.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row2.0
5th row2.0
ValueCountFrequency (%)
2.0244
38.0%
3.0199
31.0%
1.0112
17.4%
4.072
 
11.2%
5.015
 
2.3%
2021-09-23T19:39:14.084018image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:14.148815image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0244
38.0%
3.0199
31.0%
1.0112
17.4%
4.072
 
11.2%
5.015
 
2.3%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2244
 
12.7%
3199
 
10.3%
1112
 
5.8%
472
 
3.7%
515
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
2244
 
19.0%
3199
 
15.5%
1112
 
8.7%
472
 
5.6%
515
 
1.2%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2244
 
12.7%
3199
 
10.3%
1112
 
5.8%
472
 
3.7%
515
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2244
 
12.7%
3199
 
10.3%
1112
 
5.8%
472
 
3.7%
515
 
0.8%

getcovid
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2.0
615 
1.0
 
27

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0
ValueCountFrequency (%)
2.0615
95.8%
1.027
 
4.2%
2021-09-23T19:39:14.316200image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:14.377006image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0615
95.8%
1.027
 
4.2%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2615
31.9%
127
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
2615
47.9%
127
 
2.1%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2615
31.9%
127
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2615
31.9%
127
 
1.4%

satisfied
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2.0
294 
3.0
168 
1.0
149 
4.0
 
24
5.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
2.0294
45.8%
3.0168
26.2%
1.0149
23.2%
4.024
 
3.7%
5.07
 
1.1%
2021-09-23T19:39:14.549421image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:14.615200image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0294
45.8%
3.0168
26.2%
1.0149
23.2%
4.024
 
3.7%
5.07
 
1.1%

Most occurring characters

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2294
15.3%
3168
 
8.7%
1149
 
7.7%
424
 
1.2%
57
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
0642
50.0%
2294
22.9%
3168
 
13.1%
1149
 
11.6%
424
 
1.9%
57
 
0.5%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2294
15.3%
3168
 
8.7%
1149
 
7.7%
424
 
1.2%
57
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
.642
33.3%
0642
33.3%
2294
15.3%
3168
 
8.7%
1149
 
7.7%
424
 
1.2%
57
 
0.4%

region
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
3
250 
4
191 
1
156 
2
45 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters642
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row3
4th row3
5th row4
ValueCountFrequency (%)
3250
38.9%
4191
29.8%
1156
24.3%
245
 
7.0%
2021-09-23T19:39:14.801616image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:14.861374image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
3250
38.9%
4191
29.8%
1156
24.3%
245
 
7.0%

Most occurring characters

ValueCountFrequency (%)
3250
38.9%
4191
29.8%
1156
24.3%
245
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number642
100.0%

Most frequent character per category

ValueCountFrequency (%)
3250
38.9%
4191
29.8%
1156
24.3%
245
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Common642
100.0%

Most frequent character per script

ValueCountFrequency (%)
3250
38.9%
4191
29.8%
1156
24.3%
245
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII642
100.0%

Most frequent character per block

ValueCountFrequency (%)
3250
38.9%
4191
29.8%
1156
24.3%
245
 
7.0%

whovoted
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2.0
642 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1926
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0
ValueCountFrequency (%)
2.0642
100.0%
2021-09-23T19:39:15.021837image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-23T19:39:15.082634image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0642
100.0%

Most occurring characters

ValueCountFrequency (%)
2642
33.3%
.642
33.3%
0642
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
66.7%
Other Punctuation642
33.3%

Most frequent character per category

ValueCountFrequency (%)
2642
50.0%
0642
50.0%
ValueCountFrequency (%)
.642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1926
100.0%

Most frequent character per script

ValueCountFrequency (%)
2642
33.3%
.642
33.3%
0642
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1926
100.0%

Most frequent character per block

ValueCountFrequency (%)
2642
33.3%
.642
33.3%
0642
33.3%

Interactions

2021-09-23T19:38:23.497288image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:23.639902image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:23.756516image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:23.871121image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:23.985752image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:24.100365image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:24.215971image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:24.323632image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:24.435203image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:24.548853image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:24.661084image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:24.772663image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:24.885294image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:25.000937image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:25.113564image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:25.230180image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:25.342800image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:25.453429image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:25.564065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:25.670719image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:25.778299image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:25.894911image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-09-23T19:38:50.243505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:50.360126image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:50.475742image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:50.597333image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:50.728848image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:50.846489image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:50.962108image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:51.076720image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:51.193332image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:51.306963image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:51.424565image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:51.542176image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:51.899971image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:52.016583image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:52.131195image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:52.241837image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:52.354453image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:52.459104image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:52.565740image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:52.671391image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:52.778036image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:52.884682image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:52.993310image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:53.096919image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:53.201618image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:53.308254image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:53.410935image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:53.519549image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:53.626199image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:53.736854image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:53.845469image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:53.952107image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:54.054737image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:54.173361image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:54.283998image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:54.396630image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:54.508251image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:54.619872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:54.732495image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:54.843080image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:54.948770image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:55.059409image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:55.173027image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:55.282656image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:55.395288image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:55.508901image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:55.623509image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:55.732145image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:55.841793image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:55.950425image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:56.062044image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:56.165709image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:56.270354image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:56.376001image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:56.482636image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:56.587293image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:56.691946image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:56.796587image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:56.900247image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:57.007886image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:57.110543image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:57.227103image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:57.329762image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:57.446416image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:57.549085image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:57.656718image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:57.759366image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:57.869996image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:57.970619image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:58.085243image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:58.188928image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:58.294585image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:58.400226image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:58.504886image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:58.605536image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:58.704229image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:58.807818image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:58.905535image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:59.010188image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:59.112839image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:59.219497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:59.320179image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-23T19:38:59.420808image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-09-23T19:39:15.232152image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-23T19:39:16.151304image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-23T19:39:17.064218image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-23T19:39:17.933280image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-23T19:39:18.787463image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-23T19:38:59.782557image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-23T19:39:01.859721image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

caseidinterest_politicsinterest_campaignstate_regparty_regprimary_voterpol_spectrumparty_idparty_saliencegov_trustgov_interestsgov_wastegov_corruppeople_trustedgov_responsivebetter_economybetter_healthbetter_immigratinobetter_taxesbetter_environmentbetter_covidfav_deathpenstayhometrust_mediacorr_trumpfav_impeachfav_senacquittalcovid_govcovid_reopeninc_gapgov_climatesamesextransgenderlgbtlawbirthrightdeportkidswallrussianinterferereligionagemaritaleducationspouse_eduarmedforceslaborunionethnicitychildrenincomehealthgetcovidsatisfiedregionwhovoted
02005581.0120.02.02.07.07.01.04.02.02.03.04.02.05.05.05.05.04.05.01.04.02.07.07.01.03.05.03.031.02.03.07.01.01.052.053.01.04.04.03.01.02.01.01.019.03.02.03.022.0
12008311.016.01.01.01.01.04.04.01.01.03.02.03.01.01.01.02.01.01.04.03.03.01.01.07.05.02.01.016.06.01.07.06.07.019.078.01.05.03.03.05.01.01.00.017.02.02.03.042.0
22010012.0312.04.02.04.05.02.03.01.01.03.01.01.05.05.05.05.04.05.01.04.02.04.07.01.03.03.03.032.02.01.04.05.01.017.070.01.03.03.03.02.02.01.00.06.03.02.03.032.0
32010321.0240.01.02.05.05.05.04.01.01.04.02.02.04.04.05.03.02.03.02.04.02.04.07.01.03.02.03.013.02.01.07.06.02.051.072.01.04.03.03.05.02.01.00.018.02.02.01.032.0
42010631.014.02.01.07.07.01.02.01.01.03.02.01.05.05.05.05.05.05.01.04.01.02.07.01.03.03.03.031.01.04.01.05.01.052.078.01.03.03.03.05.02.01.00.010.02.02.01.042.0
52011863.0336.02.01.02.07.04.03.01.02.02.02.02.04.04.05.05.03.05.03.04.01.06.07.01.03.02.01.015.03.01.04.04.03.049.024.06.04.04.03.01.02.01.00.020.01.02.04.042.0
62013602.0125.04.02.05.05.03.02.02.03.05.03.01.05.03.05.05.04.03.01.02.02.06.07.01.03.02.02.035.01.01.04.05.01.025.080.01.02.02.03.05.02.01.00.010.03.02.01.012.0
72013772.0220.04.02.06.05.03.04.01.01.04.03.02.04.03.04.05.03.04.01.03.01.04.07.01.03.04.02.021.02.04.04.05.03.052.066.01.03.04.03.05.02.05.00.019.04.02.03.022.0
82014072.016.02.01.06.07.05.03.01.01.03.04.02.05.04.05.04.04.05.01.04.02.05.07.01.02.04.01.022.01.03.04.05.01.059.080.01.05.05.03.05.02.04.00.022.04.02.03.042.0
92014692.0125.01.01.04.07.03.04.01.02.05.03.02.05.05.05.05.05.05.01.03.04.04.04.04.03.03.01.035.05.01.04.05.01.045.036.01.05.04.03.01.01.03.02.017.03.02.02.012.0

Last rows

caseidinterest_politicsinterest_campaignstate_regparty_regprimary_voterpol_spectrumparty_idparty_saliencegov_trustgov_interestsgov_wastegov_corruppeople_trustedgov_responsivebetter_economybetter_healthbetter_immigratinobetter_taxesbetter_environmentbetter_covidfav_deathpenstayhometrust_mediacorr_trumpfav_impeachfav_senacquittalcovid_govcovid_reopeninc_gapgov_climatesamesextransgenderlgbtlawbirthrightdeportkidswallrussianinterferereligionagemaritaleducationspouse_eduarmedforceslaborunionethnicitychildrenincomehealthgetcovidsatisfiedregionwhovoted
6325229132.0236.02.02.05.06.04.04.02.01.04.02.01.04.02.02.04.02.02.02.04.04.04.04.04.03.03.01.016.06.01.03.05.07.029.066.01.04.05.03.01.02.01.00.022.02.02.02.012.0
6335239472.0137.02.02.06.07.02.03.02.01.04.02.01.05.05.05.05.05.05.04.04.01.03.07.01.03.05.03.031.01.01.01.01.01.055.069.01.04.03.02.05.02.01.00.08.02.02.01.032.0
6345247282.012.02.02.06.06.03.04.01.01.03.03.02.05.04.05.05.04.04.02.03.01.06.07.01.03.03.01.012.03.02.01.05.01.045.080.01.04.03.03.05.02.01.00.013.04.02.03.042.0
6355250042.015.01.01.02.07.02.03.01.01.03.02.03.05.05.05.04.04.04.01.03.02.04.07.01.03.04.01.031.04.01.06.02.01.052.077.01.03.03.03.05.02.01.00.05.04.02.02.032.0
6365270242.0242.02.02.04.07.04.04.01.01.03.03.02.05.04.05.05.03.05.02.02.01.06.07.01.03.05.03.022.01.04.04.05.04.054.045.01.03.03.03.07.01.01.01.01.03.02.03.012.0
6375280032.035.04.02.05.05.05.04.01.01.02.04.01.05.02.04.05.01.04.02.04.01.04.04.04.03.04.02.031.01.02.01.06.04.054.032.01.05.04.03.01.02.03.02.011.01.02.02.032.0
6385304332.0231.01.02.05.05.03.04.01.01.04.03.03.05.04.04.03.03.03.01.02.04.04.07.07.03.03.03.032.05.02.06.05.03.054.073.01.04.02.02.05.02.01.00.016.03.02.01.022.0
6395313682.016.02.01.05.07.04.05.01.01.02.02.02.05.05.05.05.05.05.02.03.01.06.07.01.03.05.03.021.03.04.04.04.01.052.040.01.01.01.03.01.02.01.02.017.02.02.01.042.0
6405332722.0121.02.02.04.06.03.04.01.01.03.04.03.05.02.04.02.01.03.04.02.01.04.07.04.05.04.01.031.01.03.01.06.01.042.049.01.01.02.03.02.02.01.01.012.04.02.04.032.0
6415353604.0216.02.02.06.06.05.04.01.02.04.02.01.04.04.04.04.04.03.02.04.02.04.04.02.04.03.01.033.01.01.02.03.04.056.052.01.04.05.03.01.02.01.00.019.03.02.01.042.0